Method and apparatus for prediction of sudden cardiac death by simultaneous assessment of autonomic function and cardiac electrical stability

ABSTRACT

A method and apparatus for predicting susceptibility to sudden cardiac death simultaneously assessing cardiac electrical stability and autonomic influence. Cardiac electrical stability is assessed by analyzing at least one of a beat-to-beat alternation in a T-wave of an ECG of a patient&#39;s heart and dispersion of repolarization in the ECG of the patient&#39;s heart. Autonomic influence on the patient&#39;s heart is assessed by analyzing at least one of a magnitude of heart rate variability in the ECG of the patient&#39;s heart and baroreceptor sensitivity.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSOREDRESEARCH AND DEVELOPMENT

Part of the work performed during development of this invention utilizedU.S. Government funds. The U.S. Government has certain rights in thisinvention.

RELATED APPLICATION

This application is a continuation-in-part of application Ser. No.07/948,529, filed Sep. 22, 1992, now as U.S. Pat. No. 5,265,617; whichis a continuation-in-part of application Ser. No. 07/768,054, filed Sep.30, 1991, now U.S. Pat. No. 5,148,812; which is a continuation-in-partof application Ser. No. 07/659,711, filed Feb. 20, 1991, now abandoned.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to cardiology. More specifically, the inventionrelates to non-invasive identification and management of individuals atrisk for sudden cardiac death. Cardiac vulnerability to ventricularfibrillation, the mode of sudden death, is dynamically tracked byanalysis of an electrocardiogram.

2. Related Art

Sudden cardiac death (SCD), which claims over 350,000 lives annually inthe United States, results from abrupt disruption of heart rhythmprimarily due to ventricular fibrillation. Fibrillation occurs whentransient neural triggers impinge upon an electrically unstable heartcausing normally organized electrical activity to become disorganizedand chaotic. Complete cardiac dysfunction results.

The first step in preventing sudden cardiac death is identifying thoseindividuals whose hearts are electrically unstable. This is a majorobjective in cardiology. If vulnerable individuals can be reliablyidentified non-invasively, then prevention will be aided, mass screeningwill become possible, and pharmacologic management of vulnerableindividuals can be tailored to prevent ventricular fibrillation.

Programmed cardiac electrical stimulation has been used in patients toprovide quantitative information on susceptibility and on theeffectiveness of their pharmacologic therapy. Unfortunately, this methodrequires cardiac catheterization and introduces the hazard ofinadvertent induction of ventricular fibrillation. Therefore, it is usedonly in severely ill patients and is performed only in hospitals. It isunsuitable for mass screening.

A technique which has shown great promise is that of analyzing alternansin the T-wave of an electrocardiogram (ECG). As used throughout thisdisclosure, the term "T-wave" is defined to mean the portion of an ECGwhich includes both the T-wave and the ST segment. Alternans in theT-wave results from different rates of repolarization of the musclecells of the ventricles. The extent to which these cells recover (orrepolarize) non-uniformly is the basis for electrical instability of theheart.

The consistent occurrence of alternans in the T-wave prior tofibrillation is well established. Thus, detection of alternans promisesto be a useful tool in predicting vulnerability to fibrillation, if anaccurate method of quantifying the alternans can be developed. Thefollowing are examples of conventional attempts to quantify alternationin an ECG signal: Dan R. Adam et al., "Fluctuations in T-Wave Morphologyand Susceptibility to Ventricular Fibrillation," Journal ofElectrocardiology, vol. 17 (3), 209-218 (1984); Joseph M. Smith et al."Electrical alternans and cardiac electrical instability," Circulation,vol. 77, No. 1, 110-121 (1988); U.S. Pat. No. 4,732,157 to Kaplan etal.; and U.S. Pat. No. 4,802,491 to Cohen et al.

Smith et al. and Cohen et al. disclose methods for assessing myocardialelectrical instability by power spectrum analysis of the T-wave. Thesemethods derive an alternating ECG morphology index from a series ofheartbeats. Sample point matrices are constructed and the alternatingenergy at each of the sample points is computed using the analyticalmethod of multi-dimensional power spectral estimation which iscalculated by constructing the discrete Fourier transform of theHanning-windowed sample auto-correlation function. The alternatingenergy over the entire set of sample points is summed to generate thetotal alternating energy and then normalized with respect to the averagewaveform to produce an "alternating ECG morphology index (AEMI)."

While a powerful tool, Fourier power spectrum analysis averages timefunctions over the entire time series so that rapid arrhythmogenicchanges, such as those due to neural discharge and reperfusion, are notdetected because data from these events are intrinsicallynon-stationary.

Kaplan et al. disclose a method for quantifying cycle-to-cycle variationof a physiologic waveform such as the ECG for the purpose of assessingmyocardial electrical stability. A physiologic waveform is digitized andsampled and a scatter plot of the samples is created. Non-lineartransformation of the sample points determines a single parameter whichattempts to quantify the degree of alternation in the sampled waveformand which is associated with the susceptibility of the physiologicwaveform to enter into an aperiodic or chaotic state. Kaplan et al.suggest that "measurement of [this parameter] may provide an index ofECG waveform variability which may provide an improved correlation withsusceptibility to ventricular fibrillation than previously availableindices." See col. 3, lines 15-19. Whether ventricular fibrillation is achaotic state, however, is still very much in debate. See D. T. Kaplanand R. J. Cohen, "Searching for chaos in fibrillation," Ann. N.Y. Acad.Sci., vol. 591, pp. 367-374, 1990.

Adam et al. disclose a non-invasive method which involves spectralanalysis of the alternation from beat-to-beat morphology of the ECGcomplex. The alternation of T-wave energy from beat-to-beat was measuredto generate a T-wave alternation index (TWAI). This technique is unableto detect alternation in waveform morphology which results inalternating wave shapes of equal energy. In addition, the amount ofalternation detected per this method is dependent on the static portionof the wave shape. That is, the same amount of alternation superimposedon a different amplitude signal will result in different values for theT-wave alternation index such that this technique could completelyobscure the presence of alternation in the original waveformmorphologies.

In the absence of an effective method for dynamically quantifying themagnitude of alternation, identification of alternans as a precursor oflife-threatening arrhythmias and provision of a test for cardiacvulnerability have been unattainable. In addition, the conventionalattempts to quantify alternans have employed inferior methods ofalternans (i.e., ECG) sensing. The ECG signals used for the Cohen et al.analysis were sensed via epicardial (i.e., heart surface) electrodes orvia lateral limb, rostral-caudal, and dorsal-ventral leads. Smith et al.sensed via leads I, aVF, and V₁₋₂. Adam et al. utilized ECG lead I"because in this lead the ratio of the amplitude of the pacing stimulusartifact to the amplitude of the QRS complex was usually smallest." SeeAdam et al. at 210. Lead I, however, provides only limited informationregarding the electrophysiologic processes occurring in the heart.

There have been occasional reports in the human literature noting thepresence of T-wave alternans in the precordial leads. However, there hasbeen no suggestion of a superior lead configuration from the bodysurface which permits measurement of alternans as a quantitativepredictor of susceptibility to ventricular fibrillation and suddendeath. For example, alternans have been observed in precordial leads V₄and V₅ during a PCTA (Percutaneous Transluminal Coronary Angioplasty)procedure on a fifty year-old man. M. Joyal et al., "ST-segmentalternans during percutaneous transluminal coronary angioplasty," Am. J.Cardiol., vol. 54, pp. 915-916 (1984). Similarly, alternans were notedin precordial leads V₄ through V₆ on a forty-four year-old man duringand following a treadmill exercise. N. Belic, et al., "ECGmanifestations of myocardial ischemia," Arch. Intern. Med., vol. 140,pp. 1162-1165 (1980).

Dispersion of repolarization has also been integrally linked to cardiacvulnerability and has recently received considerable attention as apotential marker for vulnerability to ventricular fibrillation. Thebasis for this linkage is that the extent of heterogeneity of recoveryof action potentials is directly related to the propensity of the heartto experience multiple re-entrant currents, which initiate and maintainfibrillation and culminate in cardiac arrest. B. Surawicz, "Ventricularfibrillation," J. Am. Coll. Cardiol., vol. 5, pp. 43B-54B (1985); and C.Kuo, et al., "Characteristics and possible mechanism of ventriculararrhythmia dependent on the dispersion of action potential duration,"Circulation, vol. 67, pp. 1356-1367 (1983).

The most commonly employed non-invasive approach for measuringdispersion is to obtain body surface maps to define the distribution ofT-wave isopotentials and thus estimate the degree of unevenness ofrepolarization and susceptibility to ventricular fibrillation. F.Abildskov, et al., "The expression of normal ventricular repolarizationin the body surface distribution of T potentials," Circulation, vol. 54,pp. 901-906 (1976); J. Abildskov and L. Green, "The recognition ofarrhythmia vulnerability by body surface electrocardiographic mapping,"Circulation, vol. 75 (suppl. III), pp. 79-83 (1987); and M. Gardner, etal., "Vulnerability to ventricular arrhythmia: assessment by mapping ofbody surface potential," Circulation, vol. 73, pp. 684-692 (1986).Although this approach has been in existence for over 15 years, it hasreceived minimal usage in the clinical setting. The basis for this isthat the technique is cumbersome, as it requires over 100 leads on thechest and extensive computerized analysis. Thus, it is used in only afew specialized research centers.

Recently, these has been interest in analyzing QT interval dispersion inthe standard 12-lead ECG as a measure of vulnerability tolife-threatening arrhythmias. The mathematical transformation requiredis relatively straightforward as it involves mainly subtraction of aminimum QT interval from a maximum QT interval and determining thevariance of the difference. For example, it has been found that QTdispersion is art indicator of risk for arrhythmia in patients with thelong QT syndrome, who have greatly enhanced susceptibility tocatecholamines released by the nervous system. C. Day, et al., "QTdispersion: an indication of arrhythmia risk in patients with long QT Sintervals," Br. Heart J., vol. 63, pp. 342-344 (1990). These observationwere confirmed and extended in C. Napolitano, et al., "Dispersion ofrepolarization: a marker of successful therapy in long QT syndromepatients [abstract]," Eur. Heart J., vol. 13, p. 345 (1992).

The present inventors' experimental studies have demonstrated that thevariance of T-wave dispersion in the epicardial electrogram exhibits ahighly significant predictive value in estimating risk for ventricularfibrillation during acute myocardial ischemia. R. Vettier, et al.,"Method of assessing dispersion of repolarization during acutemyocardial ischemia without cardiac electrical testing [abstract],"Circulation, vol. 82, no. III, p. 450 (1990). Furthermore, their datahas demonstrated that a linear relationship exists between theepicardial and the precordial ECG. See U.S. Pat. No. 5,148,812. Thisprovides the scientific basis for utilizing precordial T-wave dispersionas a measure of the degree of heterogeneity of repolarization whichoccurs within the heart.

Napolitano et al., supra, have shown in human subjects afflicted withthe long QT syndrome that the variance of QT interval in the sixstandard precordial leads of the ECG is more accurate than the limbleads in estimating risk of life-threatening arrhythmias. Theseinvestigators have also demonstrated that dispersion of QT interval alsoprovided a marker of successful therapy in patients receivingbeta-blockade therapy and those undergoing cervical ganglionectomy.

Within the last year, it has been demonstrated that QT intervaldispersion can predict the development of Torsades de Pointes, aprecursor arrhythmia to ventricular fibrillation in patients receivingantiarrhythmic drug therapy. T. Hii, et al., "Precordial QT intervaldispersion as a marker of torsades de pointes: disparate effects ofclass la antiarrhythmic drugs and amiodarone," Circulation, vol. 86. pp.1376-1382 (1992).

Another method which has been explored to assess autonomic nervoussystem activity, the neural basis for vulnerability to sudden cardiacdeath, is analysis of heart rate variability (HRV). Heart ratevariability, however, is not an absolute predictor of SCD because thereare major, non-neural factors which contribute to sudden death. Theseinclude: coronary artery disease, heart failure, myopathies, drugs,caffeine, smoke, environmental factors, and others. Accordingly,techniques which rely on heart rate variability to predict cardiacelectrical stability are not reliable.

Further, conventional techniques for analyzing heart rate variabilityhave relied on power spectrum analysis. See, for example, Glenn A. Myerset al., "Power spectral analysis of heart rate variability in suddencardiac death: comparison to other methods," IEEE Transactions onBiomedical Engineering, vol. BME-33, No. 12. December 1986, pp.1149-1156. As discussed above, however, power spectrum (Fourier)analysis averages time functions over an entire time series so thatrapid arrhythmogenic changes are not detected.

Complex demodulation as a method tier analyzing heart rate variabilityis discussed in Shin et al., "Assessment of autonomic regulation ofheart rate variability by the method of complex demodulation," IEEETransactions on Biomedical Engineering, vol. 36, No. 2, February 1989,which is incorporated herein by reference. Shin et al. teach a method ofevaluating the influence of autonomic nervous system activity duringbehavioral stress. A technique of complex demodulation is used toanalyze the pattern of beat-to-beat intervals to determine the relativeactivity of the sympathetic and parasympathetic nervous systems. WhileShin et al. exploited the dynamic analytical characteristics of complexdemodulation, they did not relate their results to cardiacvulnerability.

Similarly, T. Kiauta et al. "Complex demodulation of heart rate changesduring orthostatic testing," Proceedings Computers in Cardiology, (Cat.No. 90CH3011-4), IEEE Computer Society Press, 1991, pp. 159-162,discusses the use of complex demodulation to assess heart ratevariability induced by the standing-up motion in young healthy subjects.Using the technique of complex demodulation, Kiauta et al. conclude thatthe complex demodulate of the high frequency band probably reflectsparasympathetic activity, but the complex demodulate of the lowfrequency band does not seem to indicate sympathetic activity. Similarto Shin et al., Kiauta et al. do not relate their results to cardiacvulnerability.

In summary, analysis of the morphology of an ECG (i.e., T-wave alternansand QT interval dispersion) has been recognized as a means for assessingcardiac vulnerability. Similarly, analysis of heart rate variability hasbeen proposed as a means for assessing autonomic nervous systemactivity, the neural basis for cardiac vulnerability. When researchingvulnerability to sudden cardiac death, researchers have conventionallyrelied on power spectrum (Fourier) analysis. However, power spectrumanalysis is not capable of tracking many of the rapid arrhythmogenicchanges which characterize T-wave alternans and dispersion and heartrate variability. As a result, a non-invasive diagnostic method ofpredicting vulnerability to sudden cardiac death by analysis of an ECGhas not achieved clinical use.

What is needed is a non-invasive, dynamic method for completelyassessing vulnerability to ventricular fibrillation under diversepathologic conditions relevant to the problem of sudden cardiac death.Among the most significant problems are enhanced discharge by thesympathetic nervous system, behavioral stress, acute myocardialischemia, reperfusion, effects of pharmacologic agents on the autonomicnervous system, and intrinsic cardiac effects of pharmacologic agents.To accommodate these conditions, the method must not assume stationarityof data and must be sensitive to slowly varying amplitude and phase overtime. The diagnostic system must be sensitive to the fact that the areaof injury to the heart can vary significantly, that extrinsic as well asintrinsic influences affect the electrical stability of the heart, andthat the electrophysiologic end point to be detected must befundamentally linked to cardiac vulnerability.

SUMMARY OF THE INVENTION

The present invention is a method and apparatus for non-invasive,dynamic tracking and diagnosing of cardiac vulnerability to ventricularfibrillation. It is non-invasive as it detects vulnerability from leadsplaced on the surface of the chest. Tracking and diagnosis of cardiacelectrical stability are achieved through simultaneous assessment ofT-wave alternans, QT interval dispersion, and heart rate variability.The method permits tracking of transient but deadly pathophysiologicevents, such as enhanced discharge by the sympathetic nervous system,behavioral stress, acute myocardial ischemia and reperfusion.

T-wave alternans, heart rate variability and QT interval dispersion aresimultaneously evaluated. T-wave alternation is an excellent predictor(high sensitivity) of cardiac electrical instability but can beinfluenced by mechano-electrical coupling which does not influencecardiac vulnerability but reduces the specificity of the measure. QTinterval dispersion is a less accurate predictor (lower sensitivity) ofcardiac electrical instability but is not sensitive tomechano-electrical coupling. However, potential artifacts may begenerated by excessively low heart rate in QT interval dispersion or byits use of multiple leads. Heart rate variability is a measure ofautonomic influence, a major factor in triggering cardiac arrhythmias.By simultaneously analyzing each phenomenon (T-wave alternans, QTinterval dispersion and heart rate variability), the extent and cause ofcardiac vulnerability can be assessed. This has important ramificationsfor tailoring and assessing the efficacy of drug therapy.

The method includes the following steps. A heart is monitored to sensean ECG signal. The sensed ECG signal is then amplified and low-passfiltered before it is digitally sampled and stored. Estimation ofalternans amplitude and extent of dispersion and analysis of heart ratevariability are then separately performed.

Estimation of the amplitude of alternans is performed as follows. Thelocation of the T-wave in each R-R interval (heart beat) of the ECG isestimated, and each T-wave is partitioned into a plurality of timedivisions. The sampled ECG signal in each of the time divisions issummed together and a time series is formed for each of the timedivisions such that each time series includes corresponding timedivisions from successive T-waves. The time series are detrended beforefurther processing in order to remove the effects of drift and DC bias.

Dynamic estimation is performed on each time series to estimate theamplitude of alternation for each time division. The preferred method ofdynamic estimation is Complex Demodulation. Other methods includeEstimation by Subtraction, Least Squares Estimation, Auto RegressiveEstimation, and Auto Regressive Moving Average Estimation. The amplitudeof alternation is used as an indication of cardiac susceptibility toventricular fibrillation (i.e., cardiac electrical instability).

Estimation of a measure of QT interval dispersion is performed byanalyzing ECG signals taken from a plurality of electrode sites.Dispersion is determined by analyzing the ECG signals across theelectrode sites. In the preferred embodiment, one of five differentmethods may be used to estimate a dispersion measure. First, dispersionmay be computed as a maximum difference between QT intervals takenacross the plurality of electrode sites. Second, dispersion may becomputed as a maximum difference between QT intervals which have beencorrected using Bazett's formula. Third, dispersion may be estimated bya method which takes the standard deviation of a QT interval ratio.Fourth, dispersion may be estimated by a method which takes the standarddeviation of the corrected QT interval ratio. Finally, dispersion may beestimated by computing the maximum RMS (root mean square) deviation ofthe ECG waveforms recorded from a plurality of sites.

Analysis of heart rate variability is performed as follows. The apex ofeach R-wave is determined, and the time between successive R-waves iscomputed to determine a magnitude (time) of each R-R interval. Themagnitude of each R-R interval is then compared to a predeterminedcriterion to eliminate premature beats. Next, a time series of themagnitudes of the R-R intervals is formed. Dynamic estimation isperformed on the time series to estimate the magnitude of a highfrequency component of heart rate variability and to estimate themagnitude of a low frequency component of heart rate variability.

The magnitude of the high frequency component of heart rate variabilityis indicative of parasympathetic activity. The magnitude of the lowfrequency component of heart rate variability is indicative of combinedsympathetic activity and parasympathetic activity. A ratio of the lowfrequency component and the high frequency component of heart ratevariability is formed. The ratio is indicative of sympathetic activityor vagal withdrawal. In addition, recent studies have shown thatparticular emphasis should be paid to the Very Low Frequency (VLF)(0.0033 to 0.04 Hz) and Ultra Low Frequency (ULF) (<0.0033 Hz) spectralportions of heart rate variability as a powerful predictor of arrhythmiain the first two years following a myocardial infarction.

In the preferred embodiment of the invention, the ECG is sensednon-invasively via the precordial or chest leads for optimal alternansdetection. Leads V₅ and/or V₆ detect the optimal alternans signal whenthe left side (the most common site of injury for the propagation oflife-threatening arrhythmias) of the heart is ischemic or injured. LeadsV₁ and/or V₂ are optimal for detecting obstruction of the right-sidedcoronary circulation. Additional precordial leads, such as V₉, may beuseful for sensing alternans resulting from remote posterior wallinjury. A physician may use the complete precordial lead system toobtain precise information non-invasively regarding the locus ofischemia or injury.

For the dispersion measure, a plurality of chest leads (e.g., thestandard precordial or some greater number) may be used to provide aplurality of electrode sites across which dispersion may be measured.Heart rate variability is easily sensed from any of the standard EGGleads.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following, more particulardescription of a preferred embodiment to the invention, as illustratedin the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a typical ECG plot.

FIG. 1B is a typical ECG plot and action potential plot illustrating thecorrelation between dispersion of repolarization and the QT interval.

FIG. 1C shows a number of heart rate plots with corresponding spectralplots.

FIG. 2A is high-level block diagram illustrating the diagnosticprinciples of the present invention.

FIG. 2B is a block diagram illustrating the diagnostic principles of thepresent invention in a first example.

FIG. 2C is high-level block diagram illustrating the diagnosticprinciples of the present invention in a second example.

FIG. 3 is a flow chart illustrating the method of the present invention.

FIG. 4 is a flow chart detailing the process of dynamically estimatingthe amplitude of T-wave alternans (as performed in step 314 of FIG. 3).

FIG. 5A is a flow chart detailing the process of dynamically analyzingheart rate variability to determine the activity of the autonomicnervous system (as performed in step 314 of FIG. 3).

FIG. 5B is a flow chart detailing the process of dynamically analyzingheart rate variability to determine the ultra low and very low frequencyactivity of the autonomic nervous system (as performed in step 314 ofFIG. 3).

FIG. 6 is a flow chart illustrating a method for estimating first andsecond measures of QT interval dispersion.

FIGS. 7A and 7B is a flow chart illustrating a method for estimatingthird and fourth measures of QT interval dispersion.

FIG. 8 is a flow chart illustrating a method for estimating a fifthmeasure of QT interval dispersion.

FIG. 9A is a high-level block diagram of the apparatus of the invention.

FIG. 9B is a detailed block diagram of ECG detector and pre-processor902.

FIG. 9C is a detailed block diagram of ECG processing system 904comprising a microcomputer.

FIG. 10 is a detailed block diagram of the preferred embodiment of theheart monitoring unit (HMU) 900.

FIG. 11A is an ECG recorded within the left ventricle of a dog beforecoronary artery occlusion as set forth in the animal study below.

FIG. 11B shows superimposition of six successive beats from FIG. 11Apresented on an expanded time scale.

FIG. 12A is an ECG recorded within the left ventricle of a dog aftertour minutes of coronary artery occlusion as set forth in the animalstudy below.

FIG. 12B shows superimposition of six successive beats from FIG. 12Apresented on an expanded time scale.

FIG. 13A is an ECG recorded within the left ventricle of a dog afterrelease of the coronary artery occlusion (during reperfusion) as setforth in the animal study below.

FIG. 13B shows superimposition of six successive beats from FIG. 13Apresented on an expanded time scale.

FIG. 14A is a surface plot of the T-wave of the ECG for eight dogs withintact cardiac innervation showing the effects of coronary arteryocclusion and reperfusion.

FIG. 14B is a surface plot of the T-wave of the ECG for six dogs afterbilateral stellectomy showing the effects of coronary artery occlusionand reperfusion.

FIG. 14C is a surface plot of the T-wave of the ECG for eleven dogsduring thirty seconds of stimulation of the ansa subclavia of thedecentralized left stellate ganglion showing the effects of coronaryartery occlusion and reperfusion.

FIG. 15 shows the correlation between the occurrence of spontaneousventricular fibrillation and T-wave alternans in ten dogs.

FIG. 16 is a graph showing the responses of the sympathetic andparasympathetic nervous systems to a LAD coronary artery occlusion andreperfusion as indicated by heart rate variability.

FIGS. 17A-17C illustrate the positioning of the precordial ECG leads onthe body.

FIG. 18 is a cross-section of the human body illustrating thepositioning of precordial ECG leads V₁ -V₆, relative to the heart.

FIG. 19A is all ECG recorded from lead II during coronary arteryocclusion in a dog.

FIG. 19B shows superimposition of six successive beats from FIG. 19Apresented on an expanded time scale.

FIG. 20A is an ECG from precordial lead V₅ recorded simultaneously withthe ECG of FIG. 19A.

FIG. 20B shows superimposition of six successive beats from FIG. 20Apresented on an expanded time scale.

FIG. 21A is an ECG from a left ventricular intracavitary electroderecorded simultaneously with the ECG of FIG. 19A.

FIG. 21B shows superimposition of six successive beats from FIG. 21Apresented on an expanded time scale.

FIG. 22 is a graph showing the relative magnitudes of alternans signalssensed from lead II, from precordial lead V₅, and from a leftventricular intracavitary electrode.

FIG. 23 is a surface plot display obtained by the method of complexdemodulation (as set forth above) of the T-wave of the V₄ precordiallead during spontaneous heart rhythm in a representative patient duringangioplasty.

FIG. 24 shows the level of T-wave alternans as a function of recordingsite in seven patients at three minutes of angioplasty-induced occlusionand upon balloon deflation.

FIG. 25A and 25B illustrate all example positioning of a plurality ofECG leads on the body for QT dispersion measurement.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT INTRODUCTION

The invention is directed to a method and apparatus for screeningindividuals at risk for sudden cardiac death. In order to produce anoptimal testing methodology, the invention takes a receiver operatingcharacteristic (ROC) curve approach to cardiac risk stratification. Theinvention meets three criteria required for successful riskstratification and treatment:

(1) identification of subsets of patients at high risk for suddencardiac death;

(2) elucidation of specific mechanisms by which sudden cardiac deathoccurs: and

(3) identification of mechanisms at which treatment can be aimed.

The following terms are used herein:

Complex demodulation: A spectral analysis method which estimates theamount of signal in a specified frequency band by frequency translationof the signal and low-pass filtering.

Expert system: A domain-specific (e.e., medicine, engineering,accounting) computer system built to emulate the reasoning process ofthe mind of an expert in that domain.

Heart rate variability: An estimate of the frequency content ofvariation in heart rate as a measure of automatic nervous system output.

Myocardial infarction: Damage to or death of cardiac muscle, usually dueto coronary artery occlusion as a result of plaque rupture or formationof a clot.

Negative predictivity: The probability that an individual is trulydisease-free given a negative screening test. It is calculated bydividing the number of true negatives by the sum of false negatives andtrue negatives.

Neural network: A computing model which emulates to some degree thearchitecture and function of a group of neurons. The network is trainedto interpret input data by adaptive adjustment of the strength of theinterconnections.

Positive predictivity: The probability that a person actually has thedisease given that he or she tests positive. It is calculated bydividing the number of true positives by the sum of true positives andfalse positives.

Predictivity: The probability that an individual actually has thedisease, given the results of the screening test.

Sensitivity: The probability of testing positive if the disease is trulypresent. It is calculated by dividing the number of true positives bythe sum of true positives and false negatives. True positives are theindividuals for whom the screening test is positive and the individualactually has the disease. False negatives are the number for whom thescreening test is negative but the individual does have the disease.

Specificity: The probability of screening negative if the disease istruly absent. It is calculated by dividing the number of true negativesby the sum of false positives and true negatives. True negatives areindividuals for whom the screening test is negative and the individualdoes not have the disease. False positives are the individuals for whomthe screening test is positive but the individual does not have thedisease.

Sudden cardiac death: Natural death due to cardiac causes, heralded byabrupt loss of consciousness within one hour of onset of acute symptoms,in an individual with or without known preexisting heart disease, but inwhom the time and mode of death are unexpected. Sudden death is theleading form of adult mortality in the industrially developed world,claiming one death per minute in the United States alone. Coronary careunit and out-of-hospital resuscitation experience have shown that suddendeath is due primarily to ventricular fibrillation.

T-wave alternans: A regular beat-to-beat variation of the T-wave of anelectrocardiogram which repeats itself every two beats and has beenlinked to underlying cardiac electrical instability.

The preferred embodiment of the invention is discussed in detail below.While specific configurations and arrangements are discussed, it shouldbe understood that this is done for illustration purposes only. A personskilled in the art will recognize that other configurations andarrangements may be used without departing from the spirit and scope ofthe invention.

The preferred embodiment of the invention is now described withreference to the figures where like reference numbers indicate likeelements. Also in the figures, the left most digit of each referencenumber corresponds to the figure in which the reference number is firstused.

FIG. 1A shows a representative human surface ECG 100. A deflection 102is known as the "P-wave" and is due to excitation of the atria.Deflections 104, 106 and 108 are known as the "Q-wave," "R-wave," and"S-wave," respectively, and result from excitation (de-polarization) ofthe ventricles. Deflection 110 is known as the "T-wave" and is due torecovery (repolarization) of the ventricles. One cycle (i.e., cardiaccycle or heart beat) of the ECG from the apex of a first R-wave to theapex of the next R-wave is known as the R-R or interbeat interval. Heartrate variability (HRV) refers to changes in the heart rate (HR) orlength (time) of the interbeat interval from one beat to the next.

A portion 112 between S-wave 108 and T-wave 110 of ECG 100 is known asthe "ST segment" ST segment 112 includes the portion of the ECG from theend of S-wave 108 to the beginning of the T-wave 110. Because thisinvention is concerned with alternans in the ST segment as well as inthe T-wave, the term "T-wave" in this disclosure, as noted above,includes both the T-wave and the ST segment portions of the ECG. Theinventors have found that most alternation occurs in the first half ofthe T-wave, the period of greatest vulnerability to ventricularfibrillation. See, Nearing BD, Huang AH and Verrier RL. "DynamicTracking of Cardiac Vulnerability by Complex Demodulation of the TWave," Science 252:437-440, 1991.

This invention is also concerned with the QT interval. The QT intervalis defined as the period between the beginning of the Q-wave and the endof the T-wave. However, other definitions for the QT interval (e.g.,from the beginning of the Q-wave to the apex of the T-wave) may be usedwithout departing from the spirit and scope of the invention as definedin the claims.

FIG. 1B illustrates the concept of QT interval dispersion. A sample ECGsignal 150 and a corresponding cellular action potential 160 are shown.Line 152 indicates the beginning of the Q-wave. Line 154 indicates theend of the T-wave. Action potential 160 represents the cellularrepolarization occurring during the QT interval 156. Note thatdispersion 158 occurs primarily during the first half of the T-wave asillustrated between lines 162, 164. This is the period in which theheart is most vulnerable to cardiac electrical instability.

A more detailed discussion of ECG sensing and analysis is provided inDale Dubin, Rapid Interpretation of EKG's, 4th Edition, Cover PublishingCompany, 1990, which is incorporated herein by reference.

Conventionally, autonomic nervous system activity, as indicated by heartrate variability, has been researched as an independent indicator ofcardiac vulnerability (electrical stability). Autonomic nervous systemactivity, however, is not an absolute predictor of cardiacvulnerability.

Further, conventional research has evaluated heart rate variability, ECGmorphology as indicated by T-wave alternans, and ECG morphology asindicated by QT interval dispersion as independent variables indicativeof cardiac vulnerability. This also is an invalid assumption. HRV andECG morphology are linked, however, not invariably. Alternans, QTinterval dispersion and HRV can each change independently.

Heart rate variability and ECG morphology measure different aspects ofcardiovascular control. Both must be assessed in order to fully diagnosecardiac vulnerability. The inventors have discovered that simultaneousanalysis of heart rate variability, T-wave alternans and dispersionyields important diagnostic information pertaining to cardiacvulnerability. Heretofore, this information has not been available.

By "simultaneous", it is meant that the analysis of T-wave alternans,dispersion and heart rate variability is carried out on the same ECGdata. It is not necessary for this to be done at the same time. Forexample, the ECG data may be stored and the individual analysesperformed in sequence one after the other.

Cardiac vulnerability is affected by both intrinsic and extrinsicfactors. The intrinsic factors include coronary artery occlusion andcardiomyopathy. The extrinsic factors include the autonomic nervoussystem, pharmacologic agents, body chemistry (e.g.. electrolytes), andother chemicals (e.g., from cigarette smoke, caffeine, etcetera).

An intrinsic factor can make a heart electrically unstable and thereforesusceptible to SCD. T-wave alternans and dispersion are indicative ofcardiac electrical instability caused by intrinsic factors. WithoutT-wave alternans, a heart is not at risk of sudden cardiac death(ventricular fibrillation). As the magnitude of alternans increases, sodoes the risk of sudden cardiac death.

T-wave alternation is an excellent predictor of cardiac electricalstability but can be influenced by mechano-electrical coupling.Alternans measures both excitable stimulus and heterogeneity ofrepolarization of the cardiac substrate. It is an intrinsic property ofan ischemic and reperfused myocardium. However, mechano-electricalcoupling (e.g., through pericardial effusion and tamponade, abruptchanges in cycle length, drugs, and the like) which does not have aninfluence on cardiac vulnerability will influence alternation. Thus, ameasure of alternation has a high degree of sensitivity but a low degreeof specificity.

The inventors have discovered, however, that the low specificity ofalternation can be addressed using a test which simultaneously analyzesanother variable, QT interval dispersion. Dispersion is not a measure ofexcitable stimulus and is not sensitive to mechano-electrical coupling.However, its specificity is reduced in cases of low heart rate and dueto its requirement of multiple leads. The resulting combination ofalternans and dispersion yields an accurate predictor of cardiacelectrical instability caused by intrinsic factors.

Extrinsic factors may also cause or increase the electrical instabilityof the heart by causing or increasing alternans and dispersion. Theautonomic nervous system is a primary extrinsic factor which affectscardiac electrical stability. Relative changes in actions of theparasympathetic system versus the sympathetic system can increase themagnitude of alternans, resulting in an increased vulnerability to SCD.However, a change in the autonomic nervous system by itself is not anabsolute cause or predictor of cardiac electrical instability.

Heart rate variability is a measure of autonomic nervous systemfunction. Generally, decreased heart rate variability will tend toincrease the magnitude of alternans. Further, as described in detailbelow, analysis of the spectral content of heart rate variabilityindicates that the high frequency (e.g., 0.354 Hz) portion of the signalcorresponds to parasympathetic (i.e., vagal) activity while the lowfrequency (e.g., 0.08 Hz) portion of the signal corresponds to combinedsympathetic and parasympathetic activity.

A detailed discussion of heart rate modulation by the autonomic nervoussystem is provided in J. Philip Saul, "Beat-to-beat variations of heartrate reflect modulation of cardiac autonomic outflow," News inPhysiological Sciences, vol. 5, February 1990, pp. 32-36.

Referring to FIG. 1C (reproduced from Id. at page 35), Saul shows theheart rates and corresponding frequency spectra 120 for a patient with anormal heart. 122 for a patient with congestive heart failure, 124 for adiabetic patient with a peripheral neuropathy, 126 for a diabeticpatient with a cardiac autonomic neuropathy, 128 for a patient with atransplanted heart prior to re-innervation, and 130 for a patient with atransplanted heart after re-innervation. As can be seen from inspectionof these data plots, the loss of neural activity either due to diabetesor cardiac transplant is evident in the absence of normal spectra. Withreturn of normal innervation, the spectra at least partially return.

FIG. 2A is a block diagram illustrating the diagnostic principles of thepresent invention. Block 202 represents all factors which affect theelectrical function of the heart (e.g., drugs and/or diseases). Block204 represents increased heart rate variability resulting from thefactors of block 202. Block 206 represents alternation of the amplitudeof the T-wave and dispersion of the QT interval resulting from thefactors of block 202. Block 208 represents sudden cardiac deathresulting from ventricular fibrillation.

As shown, the factors of block 202 can lead to SCD in block 208 by twomajor pathways. The first pathway is from block 202, through block 206,to block 208. This results from a direct influence of the factors ofblock 202 on the electrical stability of the heart, manifest in the formof T-wave alternans and QT interval dispersion. This mode of SCD wouldoccur without a change in heart rate variability because the nervoussystem is not involved. A corollary to this is that a sudden deathprediction method which relies solely on heart rate variability wouldnot be adequate to detect SCD.

The second major pathway from the factors of block 202 to SCD in block208 is through blocks 204 and 206. This results from an influence of thefactors of block 202 on the autonomic nervous system. Drugs or heartdisease, for example, can significantly alter neural activity. This willbe expressed as changed heart rate variability. Certain changes inneural activity which increase sympathetic tone significantly increaseT-wave alternans and QT interval dispersion and therefore could resultin SCD.

The inventors have discovered that by combining an indication of heartrate variability with an indication of either T-wave alternans or QTinterval dispersion, it is possible, not only to assess risk for SCDaccurately, but also to determine whether a derangement in autonomicnervous system activity is causal. This has important clinicalsignificance as it affects both diagnosis and therapy. In the preferredembodiment, both T-wave alternans and QT interval dispersion areanalyzed in conjunction with heart rate variability.

For example, terfenadine (Seldane) is a drug widely employed for thetreatment of sinus problems. It has recently been discovered that, whenterfenadine is used in conjunction with antibiotics, SCD can result.Terfenadine has no known effects on the autonomic nervous system andconsequently does not affect heart rate variability. However, the drugcan result in alternans and torsades de pointes in isolated heartpreparations and is thus capable of directly de-stabilizing theelectrical activity of the heart. The measurement of T-wave alternansand/or QT interval dispersion is therefore an essential approach todetect susceptibility to SCD induced by a terfenadine/antibioticcombination. This is illustrated in FIG. 2B.

For another example, digitalis drugs are the most commonly used agentfor increasing the strength of contraction of diseased hearts. The drugsproduce this effect by both direct influence on the heart and throughalterations in the autonomic nervous system. In the proper therapeuticrange, there is no significant negative effect on the electricalstability of the heart. However, when the dose is either too high or thepatient's health status changes due to illness, the same dose of drugmay become toxic. It is often difficult to determine whether a patientis under-dosed or overdosed. By using a combinedalternans/dispersion/HRV analysis, it would be possible to determine atwhat point a neurotoxic influence may lead to alternans and SCD. Inparticular, high doses of digitalis decrease vagal tone and increasesympathetic activity, effects which would be clearly detected in anheart rate variability analysis. This is illustrated in FIG. 2C. Thisinformation would be a valuable asset in the therapeutic management ofthe patient.

As discussed above, traditional methods of quantifying heart ratevariability or the magnitude of alternans have relied on power spectrum(Fourier) analysis. However, power spectrum analysis is not capable oftracking many of the rapid arrhythmogenic changes which characterizeT-wave alternans and heart rate variability. In the preferredembodiment, the present invention utilizes complex demodulation toanalyze heart rate variability and T-wave alternans.

METHOD OF THE INVENTION

The method of the present invention for analyzing an ECG is nowdiscussed with reference to FIGS. 3-8.

An ECG signal containing a plurality N of R-R intervals is sensed from apatient in real time at step 302. For alternans and heart ratevariability analysis, only a single ECG signal (i.e., an ECG signalsensed from a single site) is required. For dispersion analysis,however, a plurality of ECG signals (i.e., ECG signals sensed from aplurality of sites) are required. The preferred method of non-invasivelysensing the ECG signals is discussed in detail below. Because the bodyis akin to a dipole, a large DC component will be present in the sensedECGs. This DC component is removed at step 304 with a high-pass filterprior to amplification of the ECG signals at step 306. The amplified ECGsignals are then low-pass filtered at step 308 to limit the signalbandwidth before they are digitally sampled at step 310. The digitizeddata may then be stored on a magnetic or optical storage device at step312. Finally, the digitized ECG data is processed or analyzed at step314.

Processing at step 314 involves: (1) producing an estimation ofalternans amplitude, (2) estimating the magnitude of discrete spectralcomponents of heart rate variability to determine the sympathetic andparasympathetic influences on cardiac electrical stability, and (3)determining the extend of QT interval dispersion.

As an alternative to this real-time signal pre-processing, the ECGsignals may be retrieved from the storage device (step 312) andprocessed (step 314) at a later, more convenient time.Processing/analyzing step 314 involves three independent computations:alternans processing, heart rate variability processing, and QT intervaldispersion processing. Each is discussed in detail below.

T-WAVE ALTERNANS

The analysis of alternans at step 314 is described in detail withreference to FIG. 4. At step 404, the apex of each R-wave in the signaldata for each of the N beats is located by finding the peak amplitudesin the digitized signal. Premature beats are removed at step 406 bycomparison of each R-R interval with fixed criteria. At step 408, aportion of the ECG corresponding to an estimated location (with respectto R-wave 106) of T-wave 110 is identified.

At step 410, the T-wave 110 and 112 portion of the ECG signal ispartitioned into "B" time divisions, where "B" may include a singledigital sample or a plurality of samples. The area between the EGG andthe isoelectric baseline is computed for each time division, at step412, by summing the areas of all samples in the time division. Then atstep 414, "N" successive beats (e.g., from control through release inthe animal experiments discussed below) are sequenced into a time seriesfor each of the "B" time divisions: (X(n), n=1,2 . . . N).

A high-pass filter is used for detrending the time series at step 416 toremove the effects of drift and DC bias (e.g., high-pass filteringremoves the large low-frequency variation in T-wave area that occursduring occlusion of a coronary artery). A cleaner signal is thenavailable for dynamic estimation, which is performed at step 418 toestimate the amplitude of alternation for each time series.

The estimation of step 418 may be performed via several dynamic methods.By "dynamic" method, it is meant any analytical process sufficientlyrapid to track (i.e., estimate) transient changes such as those whichoccur in alternans amplitude in response to physiologic andpathophysiologic processes triggering arrhythmias. These include, forexample, enhanced neural discharge, acute myocardial ischemia andreperfusion. A "dynamic" method should be able to track alternans fromas few as approximately ten heart beats (or less). This precludesanalytic processes (e.g., Fourier power spectrum analysis) which requirestationarity of data for several minutes. Specific, but not exclusive,examples of methods for dynamic estimation include:

(a) Complex Demodulation,

(b) Estimation by Subtraction,

(c) Least Squares Estimation,

(d) Auto-Regressive (AR) Estimation, and

(e) Auto-Regressive Moving Average (ARMA) Estimation.

(A) COMPLEX DEMODULATION

Complex demodulation is the preferred method of dynamic estimation ofthe beat-to-beat alternation in the amplitude of each time series.Complex demodulation is a type of harmonic analysis which provides acontinuous measure of the amplitude and phase of an oscillation withslowly changing amplitude and phase. It detects features that might bemissed or misrepresented by standard Fourier spectral analysis methodswhich assume stationarity of data.

By definition, alternans is a periodic alternation in the T-wave. Themagnitude of alternans, however, changes slowly during a coronary arteryocclusion and more rapidly during release, making it quasi-periodic. Assuch, it must be represented by a sinusoid with slowly varyingamplitude, A(n), and phase, φ(n):

    X(n)=A(n) cos[2πf.sub.ALT +φ(n)]                    Eq. (1)

where:

X(n)=the data sequence with alternation in its amplitude

f_(ALT) =alternation frequency (Hz). It should be noted that thisfrequency is half of the heart rate.

Using the identity

    cos(x)=e.sup.jx +e.sup.-jx /2                              Eq. (2)

the equation for X(n) can be rewritten as ##EQU1##

The method of complex demodulation requires multiplying this time seriesX(n) by two times a complex exponential at the alternans frequency [toproduce Y₁ (n)] and then filtering the result to retain only the lowfrequency term Y₂ (n) as follows: ##EQU2##

The amplitude and phase of the alternans is then found from the filteredsignal, Y₂ (n), as follows: ##EQU3## where: Im and Re refer to theimaginary and real parts of Y₂

For a more detailed discussion of complex demodulation, see FourierAnalysis of Time Series: An Introduction. by Peter Bloomfield, JohnWiley & Sons: New York, pp. 118-150: which is incorporated herein byreference.

(B) ESTIMATION BY SUBTRACTION

The subtraction method of dynamic estimation is an alternative which maybe substituted for complex demodulation. The subtraction method involvessubtracting the area of each time division (n) of an R-to-R intervalfrom the area of the corresponding time division of a subsequent (n+1),or alternatively, a previous (n-1) R-to-R interval to form a new timeseries Y(n) representing the magnitude of alternans. Because thisdifference series Y(n) may be positive or negative, the absolute valueor magnitude of Y(n) is used for the magnitude A(n). That is:

    Y(n)=X(n)-X(n-1)                                           Eq. (8) ##EQU4##

Some errors may be introduced into this estimate due to the slowlyvarying increase in magnitude of the T-wave size at the start of acoronary occlusion and the reduction in size following the occlusion.Also, some T-wave variation due to respiration is expected. Thereforedetrending the sequence X(n) using a high pass digital filter, orequivalent, improves the estimate by removing the effects of T-wave sizechanges. Also, averaging M samples together, where M is the number ofbeats occurring during a single respiratory cycle, aids in eliminatingthe respiratory effects on the estimate. Alternatively, the digitalfilter may remove both trends and respiratory changes if the respirationfrequency is sufficiently different from the heart rate, so that thefiltering does not alter the magnitude of the alternans estimate.

(C) LEAST SQUARES ESTIMATION

The least squares estimation, which also turns out, in this case, to bethe maximum likelihood estimate for estimating sinusoid amplitude inwhite noise, is a second alternative which may be substituted forcomplex demodulation to calculate a new sequence which is a dynamicestimate of the amplitude of alternans. Least squares estimation of theamplitude of alternans A(n) for the data sequence X(n) is derived asfollows.

Assume for M points (e.g., 5 to 10 cardiac cycles) that:

    X(n)=A cos(2πf.sub.ALT n)+N(n)                          Eq. (10)

where:

N(n) represents additive noise

In order to minimize the noise term and estimate the alternanscomponent, create a new function T(A), where: ##EQU5## T(A) represents ameasure of the difference between the model and the data. The bestalternans magnitude estimate results if T(A) (i.e., the noise term) isminimized. To minimize T(A), take the derivative of T(A) with respect toA and set it equal to zero: ##EQU6## Next, solve this equation for A(n)(shown simply as "A" above) and take the absolute value of the result toyield the least squares estimate of the magnitude of the alternans:##EQU7##

(D) AUTO-REGRESSIVE ESTIMATION (AR)

Auto-Regressive (AR) Estimation is a third method of dynamic estimationwhich may be substituted for complex demodulation. AR estimation modelsthe alternans as follows: ##EQU8## In this model, "P" is the number ofauto regressive coefficients chosen for the estimation. u(n) representsnoise and accounts for the imperfect fit of the estimation. The methodof estimating the amplitude of alternans A(n) for the data sequence X(n)first involves calculating a matrix of co-variance coefficients c(i,k)according to the following formula: ##EQU9## where: a=the best estimateof the true value of "a"

P=the number of auto regressive coefficients "a"

M=the number of cardiac cycles

The co-variance coefficients are then used to form "P" auto regressivecoefficients "a" as follows: ##EQU10## The estimate of the alternansmagnitude is then given by: ##EQU11##

For a more detailed discussion of auto-regressive estimation, see ModernSpectral Estimation: Theory and Applications, by Steven Kay, PrenticeHall, 1988, pp. 222-225; incorporated herein by reference.

(E) AUTO-REGRESSIVE MOVING AVERAGE (ARMA) ESTIMATION

Auto-Regressive Moving Average (ARMA) Estimation is yet another dynamicmethod which may be substituted For complex demodulation. ARMAestimation involves modeling the alternans with a data sequence X(n) asfollows: ##EQU12## Note that this equation is similar to the model ofX(n) according to the AR method, however, additional coefficients "b(k)"have been added to the model. These coefficients are necessary when thespectrum of the data has contours which are more complex than justspikes due to alternans and respiration periodicities. Let "a" and "b"be the best estimates of "a" and "b". The auto regressive coefficientestimates are found by performing Newton Raphson Iteration to find thezeros of: ##EQU13## This minimizes the error function: ##EQU14## Theestimate of the alternans magnitude is then given by: ##EQU15##

For a more detailed discussion of auto-regressive moving averageestimation, see Modern Spectral Estimation: Theory and Applications, bySteven Kay, Prentice Hall, 1988. pp. 309-312: incorporated herein byreference.

The resultant time series A(n), representative of the magnitude ofalternans, which is produced in step 418 (by one of the dynamic methodsset forth above), may then be analyzed for diagnostic purposes. This mayinclude producing a surface plot as shown in FIGS. 14A-C (describedbelow).

It will be understood by one skilled in the art that the various stepsof filtering set forth above may be performed by analog or digital meansas discussed below. It will further be understood that each of thevarious filtering steps may be modified or eliminated from the method,if desired. Note, however, that detrending is particularly important forthe Least Squares Estimate Method.

Elimination of the various filtering steps will, of course, lead to areduction in clarity and will add corruption to the sought aftersignals. The amount of corruption will depend on the amount of noisepresent in the specific data. The noise sources sought to be filteredinclude: white noise, respiration induced electrical activity, prematurebeats, slowly varying trends present in the area under the ECGwaveforms, and other miscellaneous noises.

HEART RATE VARIABILITY

The analysis of heart rate variability at step 314 is described indetail with reference to FIGS. 5A and 5B. Referring first to FIG. 5A, afirst method of analysis is described. At step 504, the apex of eachR-wave in the signal data for each of the N beats is located by findingthe peak amplitudes in the digitized signal. At step 506, the R-Rintervals (time) between successive R-waves is computed. Premature beatsare then removed at step 508 by comparing each R-R interval with fixedcriteria.

At step 510, a time series of R-R interval data is formed by listing theR-R interval times in order. At step 512, a second time series orsequence (R_(t)), whose points are 100 msec apart and whose values arethe R-R intervals present at that time, is formed along the same timeline. For example, if the R-R interval data for a certain EGG signal hasthe values:

    300 msec, 350 msec, 400 msec . . .

then the series (R_(t),t) would become:

    (300,0), (300,100), (300,200), (350,300), (350,400), (350,500), (350,600), (400,700), (400,800), (400,900), (400,1000) . . .

At step 514, the sequence (R_(t)) is filtered to remove any lowfrequency trends. A cleaner signal is then available for dynamicestimation, which is performed at steps 516 and 522 to estimate themagnitude of discrete spectral components of heart rate to determine thesympathetic and parasympathetic influences on cardiac electricalstability. This dynamic estimation at steps 516 and 522 is performedusing similar methods (except for Estimation by Subtraction) to thosediscussed above with respect to analysis of alternans at step 418.

Specifically, the estimation at steps 516 and 522 may be performed viaComplex Demodulation, Auto-Regressive (AR) Estimation, Auto-RegressiveMoving Average (ARMA) Estimation, or other time domain methods.Traditional power spectrum (Fourier) analysis may be used, however, itis not recommended because it will produce interior results and somedata (e.g., rapid changes in heart rate) may be lost.

Complex demodulation is the preferred method of demodulating heart ratevariability. Complex demodulation of heart rate variability is performedas follows. At step 516, the sequence (R_(t)) (from step 514) ismultiplied by 2·e.sup.(-j2πfn), at f≈0.10 Hz to yield the low frequencycomponent of heart rate variability. "n" is the index of the data pointin sequence (R_(t)). In parallel with the computation of the lowfrequency component of heart rate variability at step 516, the highfrequency component of heart rate variability is computed at step 522 bymultiplying the sequence (R_(t)) by 2·e.sup.(-j2πfn), at f≈0.35 Hz(i.e., a frequency close to the respiration frequency). The lowfrequency component of heart rate variability is then low pass filtered(e.g., roll-off frequency≈0.10 Hz) at step 518. The high frequencycomponent of heart rate variability is low pass filtered (e.g., roll-offfrequency≈0.15 Hz) at step 524. It should be noted that low passfiltering (steps 518 and 524) is part of the method of complexdemodulation (steps 516 and 522).

The magnitude of the high frequency (e.g.,≈0.35 Hz) component of heartrate is indicative of parasympathetic activity. The magnitude of the lowfrequency (e.g., ≈0.10 Hz) component of heart rate, however, is affectedby both sympathetic and parasympathetic activity. Therefore, to discernthe influence of the sympathetic nervous system, the low frequency (LF)component of heart rate (from step 518) is divided by the high frequency(HF) component of heart rate (frown step 524) at a step 520 to produce aratio (LF/HF). This ratio is indicative of the ratio of sympatheticactivity to parasympathetic activity and can thus be used to assesssympathetic activity. Ratioing low and high frequency components ofheart rate to estimate sympathetic activity is further described in M.Pagani, et al., "Power spectral analysis of heart rate and arterialpressure variabilities as a marker of sympatho-vagal interaction in manand conscious dog," Circulation Research, vol. 59, No. 2, August 1986,pp. 178-193, incorporated herein by reference.

Steps 516, 518 and 522, 524 of the method described above detect heartrate variability using the method of complex demodulation. Analysis ofheart rate variability using the method of complex demodulation isfurther described in Shin et al., discussed above.

Recently, there has been empirical evidence suggesting that particularemphasis should be paid to the Very Low Frequency (VLF) (0.0033 to 0.04Hz) and Ultra Low Frequency (ULF) (<0.0033 Hz) spectral portion of heartrate variability as a powerful predictor of arrhythmia in the first twoyears following a myocardial infarction. The basis for the predictivevalue of there endpoints is uncertain, as VLF and ULF appear to reflectaltered cardiac sensory input, neural efferent activity, cardiacresponsiveness, renin-angiotensin control, impaired baroreflexsensitivity and perhaps other factors. See, for example, J. Bigger, etat., "Frequency Domain measures of heart period variability to assessrisk late after myocardial infarction," J. Am. Coil. Cardiol., vol. 21,pp. 729-731 (1993).

Thus, it may be desirable to also analyze the very low frequency andultra low frequency components of heart rate variability at least as anindicator of baroreceptor sensitivity. The method for estimating themagnitude of the VLF and ULF components of heart rate variability isdescribed with reference to FIG. 5B. Steps 504-514 are identical tosteps 504-514 of FIG. 5A. Steps 526 and 532 are substantially the sameas steps 516 and 522, respectively, of FIG. 5B. That is, steps 526,532estimate the amplitude of certain spectral components of heart ratevariability. These steps may be performed according to any of themethods previously described. However, for simplicity, the steps aredescribed using complex demodulation which is the preferred embodiment.

At step 526, the sequence (R_(t)) (from step 514) is multiplied by2·e.sup.(-j2πfn), f≈0.00165 Hz to yield the ultra low frequencycomponent of heart rate variability. In parallel with this computation,the very low frequency component of heart rate variability is computedat step 532 by multiplying the sequence (R_(t)) by 2·e.sup.(-j2πfn), atf≈0.022 Hz. The ultra low frequency component is low pass filtered(e.g., roll-off frequency≈0.00165 Hz) at step 528. The very lowfrequency component is low pass filtered (e.g., roll-off frequency≈0.018Hz) at step 534. It should be noted that low pass filtering (steps 528and 534) is part of the method of complex demodulation (steps 526 and532). Empirical evidence suggests that either the Very Low Frequency orthe Ultra Low Frequency spectral portions of heart rate variability maybe indicative of baroreceptor sensitivity, a powerful predictor ofarrhythmia.

Moreover, baroreflex sensitivity (gain) may be analyzed directly as anadditional indicator of cardiac electrical stability. The baroreflexsensitivity may be non-invasively characterized as follows. First, anECG signal, a signal indicative of arterial blood pressure, and a signalrepresenting instantaneous lung volume are digitized. The ECG signal maybe processed in accordance with the method of FIG. 3 prior todigitization. In addition, the peak amplitude for each R-R interval isdetermined to locate the apex of each R-wave and premature beats areremoved. The R-R intervals may then be computed. Next, an instantaneousheart rate is computed for each R-R interval.

An autoregressive moving average model (discussed in detail above) isused to characterize the present heart rate as a function of past heartrate, past lung volume, past arterial blood pressure plus a non-specificnoise component using the following formula: ##EQU16## where: N, M and Prepresent the number of previous beats; and a, b and c represent theARMA coefficients. The ARMA model is then used with the measured ECG,blood pressure and lung volume values to estimate values for thecoefficients a,b and c. The coefficients can then be used to determinethe baroreflex gain transfer function and the static and dynamicbaroreflex gain.

QT INTERVAL DISPERSION

QT interval dispersion may be computed spatially (across a plurality ofECG leads) or temporally (across plurality of beats from a single ECGsignal). In the preferred embodiment, QT interval dispersion is computedboth temporally and spatially. The dispersion is computed by analyzingthe QT interval across a series of electrode sites/signals. However, thebeats from each ECG site/signal may be averaged prior to measuring thedispersion across several leads.

In the preferred embodiment, a dispersion measure or estimation iscomputed using one of five methods. These methods are illustrated inFIGS. 6, 7A, 7B and 8 and described below. Referring first to FIG. 6, aplurality N of ECG signals from N electrode sites are simultaneouslydigitized in a step 602. This step represents steps 302-310 of FIG. 3.In a step 604, the peak amplitude is determined for each R-R interval tolocate the apex of each R-wave. The apex of each R-wave is then used atstep 606 to determine the temporal location of the apex of each R-wave.Once the R-wave in each R-R interval has been located, the temporallocation of the beginning of each Q-wave may be determined at step 608.Premature beats are removed at step 610. At step 612, the temporallocation for the end of each T-wave is determined. The QT interval isthen computed as a time difference from the beginning of the Q-wave tothe end of the T-wave at a step 614.

At step 616, each R-R interval is computed. The QT intervals from step614 and the R-R interval from step 616 may then be used at step 618 tocalculate a corrected QT interval QT_(C) for each ECG signal (electrodesite) using Bazett's formula: ##EQU17## At step 620, the first measureof dispersion (Dispersion₁) is computed as the maximum differencebetween the QT intervals taken across the N electrode sites. Similarly,at step 622, an estimate for the second measure of dispersion(Dispersion₂) is computed by taking the maximum difference between thecorrected QT intervals across N electrode sites. Essentially, in steps620 and 622, the minimum QT interval is subtracted from the maximum QTinterval to yield a maximum difference. The maximum differences for theQT intervals and the corrected QT intervals are then used as the firstand second measures of dispersion.

FIGS. 7A and 7B illustrate the method for computing the third and fourthmeasures of dispersion. Steps 702-718 are substantially identical tosteps 602-618 of FIG. 6. At step 720, an average QT interval is computedacross the N electrode sites. At step 724, a ratio is computed for eachQT interval by dividing by the average QT interval computed at step 720.An average QT ratio is then computed at step 728 by averaging the QTratios of step 724 across the N electrode sites. Finally, at step 732, astandard deviation of the QT ratio is computed. This standard deviationis used as the third measure of dispersion (Dispersion₃).

Steps 722,726, 730, and 734 are substantially identical to steps 720,724, 728 and 732, respectively. However, the corrected QT intervals fromstep 718 are used in steps 722,726, 730 and 734 to produce a fourthmeasure of dispersion (Dispersion₄)based on the standard deviation ofthe QT_(C) ratio.

FIG. 5 illustrates the fifth method of estimating a dispersion measure.Steps 802-806 are substantially identical to steps 602-606 of FIG. 6. Atstep 808, premature beats are removed from each EKG signal. At step 810,an average ECG waveform is computed for each R-R interval using the Nelectrode sites. At step 812, the RMS (root mean square) deviation ofthe N ECG signals is computed from the average ECG waveform of step 810.At step 814, the fifth measure of dispersion (Dispersion₅) is taken asthe maximum RMS deviation for each beat.

ROC curves involving any two or all three of the parameters (i.e.,alternans, dispersion and heart rate variability) may be constructed toincrease the specificity of the method of the invention.

APPARATUS OF THE INVENTION

The preferred embodiment of the apparatus of the invention is describedwith reference to FIGS. 8 and 9. Steps 304-308 of the method may beperformed using a conventional ECG machine or may be performed usingdedicated hardware. Similarly, steps 312 and 314 may be performed on ageneral purpose computer or may performed by dedicated hardware.

In the preferred embodiment, the invention is carried out on a heartmonitoring unit (HMU) 900, shown in FIG. 9A. HMU 900 includes ECGsensing leads 901, an ECG detector and pre-processor 902 and an EGGprocessing system 904. ECG detector and pre-processor 902, shown ingreater detail in FIG. 9B, includes a high-pass filter 9022, apre-amplifier 9024, and a low-pass filter 9026. EGG sensing leads (i.e.,electrodes) 901 provide a signal from a patient directly to high-passfilter 9022.

In an alternate embodiment, ECG detector and pre-processor 902 is aconventional ECG monitoring machine.

Referring now to FIG. 9C, ECG processing system 904 is described. ECGprocessing system 904 includes a programmed microcomputer 9040 equippedwith an analog-to-digital (A/D) conversion board 9050. The steps of themethod are performed using a software program written in C Programminglanguage. The program follows the steps set forth above. It is believedthat any skilled programmer would have no difficulty writing the codenecessary to perform the steps of this invention.

Microcomputer or computer platform 9040 includes a hardware unit 9041which includes a central processing unit (CPU) 9042, a random accessmemory (RAM) 9043, and an input/output interface 9044. RAM 9043 is alsocalled a main memory. Computer platform 9040 also typically includes anoperating system 9045. In addition, a data storage device 9046 may beincluded. Storage device 9046 may include an optical disk or a magnetictape drive or disk.

Various peripheral components may be connected to computer platform9040, such as a terminal 9047, a keyboard 9048, and a printer 9049.Analog-to-digital (A/D) converter 9050 is used to sample an ECG signal.A/D converter 9050 may also provide amplification of the ECG signalprior to sampling.

FIG. 10 shows the preferred embodiment of HMU 900. The system includes16 channels to allow simultaneous monitoring of a plurality of ECGleads. High-pass filters 1004, pre-amplifiers 1006, and low-pass filters1008 perform steps 304, 306 and 308, respectively. High-pass filters1004 have a 0.01 Hz roll-on. Low-pass filters 1008 have a 50 Hzbandwidth.

A personal computer 1010 includes an A/D converter (with programmablegain), a printer 1014, a re-writable optical disk 1016, and a colormonitor 1018. The program which runs on computer 1010 is preferablymenu-driven. A sample menu is shown on monitor 1018.

The menu-driven program may take, as input, information on a patient'sage, sex, medical history, and heart rate. This information could thenbe used to select a range of standard indices (discussed below) to beused for comparison. The menu program would further allow theclinician/operator to select the A/D sampling rate, the number of EGGchannels to monitor, and the gain of the A/D converter prior tocommencing data collection. Thereafter, the clinician/operator couldmanually control removal of trends and premature beats prior toperforming the dynamic analysis of alternans, and heart ratevariability, and QT interval dispersion.

Features of the menu-driven program may include selecting the method ofdynamic analysis to be used and selecting the results to be displayed.For example, the clinician/operator may desire to view the ECGwaveforms, the time series data (e.g., for each bin of the T-wave bothbefore and after detrending for the alternans analysis; or for the R-Rintervals in the HRV analysis), or the actual estimate data (e.g.,alternans magnitude, HRV high frequency component, HRV low/highfrequency component ratio, dispersion estimate result).

In the preferred embodiment, the heart monitoring unit may employ anexpert system or neural network for the data analysis. An expert systemwill allow the monitoring unit perform complex diagnostic analyses. Theprogram may construct ROC curves based on ally two or all three of theparameters discussed above (i.e., alternans, dispersion and heart ratevariability).

ANIMAL STUDY FOR ALTERNANS ANALYSIS

Animal studies were conducted by the inventors at Georgetown UniversitySchool of Medicine in Washington, D.C. Sixteen adult mongrel dogs (20 to30 kg) of both sexes were studied in accordance with the standards ofthe scientific community. The animals were pre-medicated with morphinesulfate (2 mg/kg, subcutaneously) and anesthetized with alpha-chloralose(150 mg/kg, intravenously), with supplemental doses of alpha-chloralose(600 mg in 60 ml saline) as required. A left thoracotomy was performedvia the fourth intercostal space.

A Doppler flow probe was placed around the left anterior descending(LAD) coronary artery and occlusions were performed using a 2-0 silksnare. Aortic blood pressure was measured with a Gould-Statham P50pressure transducer. The ECG was obtained using a 7 French USCIquadripolar catheter with an inter-electrode distance of 10 mm and anelectrode width of 2 min. The catheter was positioned in the apex of theleft ventricle via a carotid artery to coincide with the ischemia. Thiscatheter placement was found to produce optimal EGG sensing.

Bipolar ECG's were obtained with the negative pole being the secondelectrode of the catheter and the positive pole being a needle-electrodeplaced transcutaneously in the lower left hip region. A pigtail pressurecatheter was positioned to monitor left ventricular (LV) blood pressure.The area under the LV pressure pulse of successive beats was analyzedusing the technique of complex demodulation. No evidence of mechanicalalternans was found. The electrocardiographic and hemodynamic data werecontinuously recorded on a Thorn EMI FM tape recorder (45 to 50 db S/Nratio, bandwidth of each channel 0 to 625 Hz). Arterial blood pH, pC0₂,and p0₂ were monitored using an Instrumentation Laboratory 1304 bloodgas analyzer and were maintained within physiologic ranges by adjustingventilation parameters of the Harvard respirator.

A bilateral stellectomy was performed to interrupt sympathetic neuralinput to the heart. This was accomplished by removal of the rightstellate ganglion via the right second interspace and by sectioning thepreganglionic fibers and the caudal end of the left ganglion through theleft thoracotomy. The ansae subclavia were left intact to permit pacingof the heart at a rate of 150 beats per minute. Pacing was accomplishedby delivering electrical stimuli of 1.5 to 2 mA of 5 ms duration at afrequency of 10 Hz to the nerves with a Grass S44 stimulator and an SIU7stimulus isolation unit.

At the end of each experiment, the taped data was low-pass filtered tolimit the signal bandwidth to 50 Hz. The data was then digitized at 500samples per second, with a Compaq 386 computer equipped with a MetrabyteDAS-20 A/D conversion board, and stored on an optical disk. The apex ofeach R-wave for each of the N beats was then located by finding the peakamplitudes in the digitized signal. Each beat was indexed by n from 1 toN. The R-R interval was employed to sort out and remove premature beatswhich could introduce artifactual spikes. Tile period from 60 to 290 msfollowing the apex of each R-wave was determined to coincide with thelocation of the T-wave. This period was divided into bins 10 ms wide foreach successive beat, and the area between the EGG and the isoelectricbaseline was computed for each 10 ms interval. N successive beats fromcontrol through release were then sequenced into a time series for eachof the 23 10-ms bins: (X(n), n=1,2, . . . N). A sixteenth orderButterworth filter was used for both detrending and demodulating toremove the large low-frequency variation in T-wave area that occursduring occlusion and to leave a cleaner signal for spectral estimation.

Detrending was performed by low-pass filtering each time series with theButterworth filter and then subtracting the result from the originaltime series to achieve a high-pass filtering function. To obtainestimates of the magnitude of beat-to-beat alternation in the amplitudeof each of these twenty-three time series, complex demodulation (as setforth above) was used.

The effects of LAD coronary artery occlusion and reperfusion on T-wavealternans were tested before and after sympathetic denervation andstimulation. Baseline data was obtained for four minutes, the artery wasoccluded for eight minutes followed by abrupt release (reperfusion) anda 30-minute rest period. As set forth above, heart rate was maintainedconstant by atrial pacing at 150 bpm during assessment of the magnitudeof alternans.

In eight dogs, a preconditioning occlusion was followed by a controlocclusion with nerves intact. The occlusion-release sequence wasrepeated after stellate ganglion ablation. Finally, the left stellateganglion was stimulated two to three minutes prior to occlusion, duringthe second and fifth minutes of occlusion, and during reperfusion. Inthe second group of eight dogs, the order of interventions was changedto rule out sequence-related error by omitting the occlusion with nervesintact.

FIGS. 11A-13A show, respectively, an electrocardiogram recorded withinthe left ventricle before, during, and after coronary artery occlusionin a single representative animal. FIGS. 11B-13B show superimposition ofsix successive beats. Prior to occlusion (FIG. 11), the T-waves of eachsucceeding beat are uniform. After four minutes of coronary arteryocclusion (FIG. 12), there is marked alternation of the first half ofthe T-wave, coinciding with the vulnerable period of the cardiac cycle.The second half of the T-wave remains uniform. After release of theocclusion (FIG. 13), alternans is bidirectional, with T-wavesalternately inscribed above and below the isoelectric line.

Coronary artery occlusion and reperfusion both resulted in significantincreases in the magnitude of beat-to-beat alternation in T-waveamplitude. FIG. 14 shows a surface plot display derived by complexdemodulation of the T-wave of the electrocardiogram before, during, andafter coronary artery occlusion in eight dogs with intact cardiacinnervation (FIG. 14A); after bilateral stellectomy in six dogs (FIG.14B); and during 30 sec of stimulation of the ansa subclavia of thedecentralized left stellate ganglion in eleven dogs (FIG. 14C).

The increase in alternans was evident within two to three minutes ofocclusion and progressed until the occlusion was terminated at eightminutes. Upon reperfusion, there was an abrupt increase in alternanswhich lasted less than one minute. A remarkable feature is that thepattern of alternation during reperfusion was bi-directional, withT-waves occurring alternately above and below the isoelectric line (FIG.13).

The time course of onset and offset of T-wave alternans during theocclusion-release sequence coincides with the spontaneous appearance ofmalignant tachyarrhythmias including ventricular fibrillation. FIG. 15shows a correlation between the occurrence of spontaneous ventricularfibrillation and T-wave alternans in ten dogs. Dogs which fibrillatedexhibited a rapid rise in alternans within the first three or fourminutes of occlusion and this change was significantly more marked thanthat observed in animals which survived the entire occlusion-releasesequence (*=p<0.001. Values are means±S.E.M.). The results were analyzedusing a one-way ANOVA with Scheffe correction for multiple comparisons.In both groups, the control values did not differ significantly from thenormal distribution by the Kolmogorov-Smirnov test.

It is noteworthy that alternans is marked, though short lasting, duringreperfusion. This transient period of heightened vulnerability tofibrillation is thought to be due to liberation of washout products ofcellular ischemia. The differing mechanisms responsible forvulnerability during occlusion and reperfusion may account for thecontrasting alternation pattern in T-wave morphology.

The studies demonstrate that the sympathetic nervous system exerts aprominent effect on T-wave alternans, a finding which is consistent withits established arrhythmogenic influence. During coronary arteryocclusion, stellectomy (FIG. 14B) reduced alternans during the earlyphase of occlusion [from 15.8±6.6 at 4 minutes during control to 4.7±1.0mV×ms (means ±S.E.M., p<0.05)], coinciding with the time when neuralactivity is high in intact animals. However, later in the occlusions,extra-adrenergic factors may play a role.

Sympathetic neural influences during the reperfusion phase also appearto be tracked reliably by the present techniques. It was observed thatstellate ganglion ablation increased T-wave alternans during reperfusion[from 19.8 ±3.0 to 29.8±3.3 mV×ms (p<0.02)]. This concurs with aprevious study indicating that stellectomy enhances reperfusion-inducedvulnerability to fibrillation. Stellate ganglion stimulation restoredthe magnitude of alternans to a value which was not statisticallydifferent from pre-denervation levels.

The link between alternans and vulnerability is underscored by thefinding that alternans coincides with the established timing of thevulnerable period in the cardiac cycle. Superimposition of successivebeats indicates that alternation is restricted to the first half of theT-wave (FIGS. 11B-13B). This relationship remained constant in allanimals studied under the changing conditions of sympathetic nervoussystem stimulation or denervation.

ANIMAL STUDY FOR HEART RATE VARIABILITY ANALYSIS

An additional animal study conducted by the inventors was performed toverity the correlation between heart rate variability and alternans.This additional study was performed substantially as set forth above.Six adult mongrel dogs were used. LAD occlusion for ten minutes wasfollowed by abrupt release. T-wave alternans appeared within threeminutes of occlusion and increased to 8.97±1.58 mVolts·reset by thefourth minute coinciding with maximum changes in parasympathetic (HF)activity and in the ratio of sympathetic to parasympathetic (LF: HF)activity. This is illustrated in FIG. 16, where 1602 representsparasympathetic activity (HF component) and 1604 represents the ratio ofsympathetic to parasympathetic activity (LF:HF ratio). As can be seenfrom inspection, sympathetic activity increases during occlusion whileparasympathetic activity decreases. At reperfusion, there is no changein autonomic activity.

It is important to note that these observations concur precisely withprevious studies in which nerve activity to the heart was measured usingrecording electrodes and vulnerability to ventricular fibrillation wasassessed by programmed cardiac electrical stimulation. In theseexperiments, it was shown that a major increase in sympathetic activitycorresponded to increased susceptibility to ventricular fibrillation.See F. Lombardi, R. L. Verrier, B. Lown, "Relationship betweensympathetic neural activity, coronary dynamics, and vulnerability toventricular fibrillation during myocardial ischemia and reperfusion,"American Heart Journal. vol. 105. 1983, pp. 958-965. A major advantageof the method of the invention is that information derived in suchprevious invasive studies can be obtained completely from the bodysurface ECG by combining heart rate variability and T-wave alternansmeasurements.

CLINICAL APPLICABILITY

An ECG suitable for the analysis of heart rate variability is easilymeasured using standard surface electrode configurations. However,alternans and dispersion require more sophisticated sensing techniques.

With respect to alternans, the inventors have discovered thatpositioning the ECG sensing electrode into the apex of the leftventricle produces an optimal ECG signal for sensing alternans. Thisintracavitary electrode placement, however, requires invasive andhazardous procedures such that its clinical, diagnostic applicability islimited. What is needed is a method for sensing T-wave alternansnon-invasively on the surface of the body.

Before discussing sensing of the electrical activity of the heart, it ishelpful to understand a few basic principles. The electrical signalsthat are sensed as an ECG include electrical currents that flow throughthe body as a result of depolarization and repolarization of themyocardial cells. This electrical activity may be sensed as a voltagebetween areas of the body (e.g., between the chest proximate the heartand an arm or leg).

Theoretically, the voltage "V" at a position (x_(p),y_(p),z_(p)) due toa charge "q" at (x_(i),y_(j),z_(k)) is given by the following equation:##EQU18## It is assumed that V_(ref) is zero for a unipolar electrode,as discussed below. If the heart is modelled as a collection of chargesthen the equation directly below will approximate the voltage V_(norm)sensed by an electrode located at a point (x_(p),y_(p),z_(p)). ##EQU19##

Under stable repolarization/depolarization, the charges of the heartwill repeat almost identically to create a stable ECG signal. That is,the charge distribution occurring x msec after the R-wave of one cardiaccycle will be nearly identical to the charge distribution occurring xmsec after the R-wave of the next cardiac cycle.

When alternans is present, however, the charge distribution will bemodulated such that the charge distribution occurring x msec after theR-wave of successive cardiac cycles can be modeled as a static chargedistribution plus a time varying distribution representing the source ofthe alternans. This time varying charge distribution resulting fromalternans may be represented by:

    q.sub.alternans =q cos(2πf.sub.ALT t)                   Eq. 26)

where:

q=the magnitude of the alternating charge

f_(ALT) =alternation frequency (Hz)

t=0, 1, 2, . . . number of beats

Locating the alternans charge at (0,0,0) produces an oscillating voltageat (x_(p),y_(p),z_(p)) as follows: ##EQU20## This results in a totalvoltage at point (x_(p),y_(p),z_(p)) of:

    V.sub.total =V.sub.norm +V.sub.alternans                   Eq. (28)

V_(total) consists of an alternating component plus a constantcomponent. To maximize the amount of alternating component detected.(x_(p),y_(p),z_(p)) must approach (0,0,0). That is, the detectingelectrode must be located as close as possible to the portion of theheart thin is generating the alternation signal.

For sensing a normal EGG, limb leads, such as lead II (left leg withrespect to right arm) can be used. Limb leads, however, are incapable ofdetecting the small amplitudes of alternans. Interestingly, theinventors have discovered that alternans is a regional phenomenon thatcan be reliably detected via the precordial EGG leads.

By regional, it is meant that the alternans emanate from the injured orischemic portion of the heart. For example, it was found that thealternation signal is strongest in the left ventricle (LV) intracavitaryEGG during a left anterior descending (LAD) coronary artery occlusion.In fact, it was noted that alternation is twelve times greater asrecorded from a LV intracavitary catheter as compared with a rightventricle (RV) intracavitary catheter. Corresponding to this discovery,the inventors have found that alternans could be detected in theprecordial surface EGG leads corresponding to the injured portion of theheart. Note that the terms "lead" and "electrode" are usedinterchangeably herein.

The precordial or chest leads are unipolar electrodes which sense theEGG signal at the surface of the body. A unipolar electrode senses apositive electrical current with respect to a neutral lead. The neutrallead is an average of the voltage on the three standard limb leads: leftleg, left arm, and right arm. Ideally, the voltage on the neutral leadis zero.

The location of the precordial leads on the body surface is shown inFIGS. 17A-17C. The precordial leads include leads V₁ through V₉ for theleft side of the body and leads V_(1R) through V_(9R) for the right sideof the body. Note that lead V₁ is the same as lead V_(2R) and that leadV₂ is the same as lead V_(1R).

The present invention is concerned primarily with precordial leads V₁through V₆ because they are closest to the heart and, therefore, yieldthe strongest ECG signals. FIG. 18 is a cross-sectional view of thehuman chest area 1802 taken along a horizontal axis 1702 shown in FIGS.17A and 17B. FIG. 18 illustrates the position of the heart 1804 inrelation to front chest wall 1806. The relative positions of precordialleads V₁ through V₆ and the corresponding normal ECG signals present ateach position are also shown. Note that lead V₅ resides directly overthe left ventricular surface.

The inventors have discovered that leads V₅ and/or V₆ are optimal forsensing alternans which result from injury to the left ventricle (e.g.,obstruction of the left anterior descending artery), and leads V₁ and/orV₂ are optimal for sensing injuries such as obstruction of theright-side coronary circulation. Additional precordial leads such as V₉,may be useful for sensing alternans resulting from remote posterior wallinjury. Thus, a physician may use the complete precordial lead system toobtain precise information regarding the locus of ischemia or injury.

In order to achieve the maximum sensitivity for alternans sensing,attenuation by the skill and other body tissues must be reduced.Attenuation by the relatively large impedance provided by the skin canbe overcome by proper skin abrasion, electrode .jelly, or the use ofneedle electrodes. Further reduction in attenuation can be achieved byselecting the path of least resistance to the heart. This includesplacing the electrodes between the ribs rather than over them.

FIGS. 19A-21A show continuous ECG tracings obtained simultaneously fromlead II, lead V₅, and a left ventricular intracavitary lead,respectively, during LAD coronary artery occlusion in achloralose-anesthetized dog. FIGS. 19B-21B show superimposition of thesuccessive beats of FIGS. 19A-21A, respectively. Note that thesuperimposed waveform from lead II (FIG. 19B) shows no consistentlydetectable alternans. Lead V₅ (FIG. 20B), however, shows markedalternation in the first half of the T-wave, corresponding to thealternation observed in the intracavitary lead (FIG. 21B).

Simultaneous comparison of T-wave alternation from lead II, lead V₅, anda left ventricular intracavitary lead during LAD coronary arteryocclusion in seven dogs was performed. The results are shown graphicallyin FIG. 22 as a comparison of alternans energy from Leads II and V₅ withreference to the LV intracavitary lead. Exact correlation with theintracavitary lead will produce a line with a 45° angle. The significantlinear relationship (r² =0.86) between signals detected in V₅ and the LVintracavitary lead indicated that the precordial lead can be used as asurrogate, obviating the need to place a catheter in the heart. Theslope in V₅ (0.17±0.05) was significantly greater than in lead II(0.08±0.02) (p<0.001). This finding is consistent with Equation 22 withpredicts a linear relationship between the detecting electrode and thesource. As shown, the signal from lead V₅ is clearly larger than that oflead II. The intracavitary lead provides a stronger signal than bothlead II and V₅.

Under certain clinical conditions, it may be advantageous to recordalternation from the right ventricle (RV) because of the nature of thecardiac pathology. For example, under conditions of right hearthypertrophy or other pathology, or right coronary artery disease, themaximum expression of alternation may be detectable from a catheterpositioned in the RV. Since a catheter can be positioned from the venousside of the circulation, the RV catheterization is relatively low riskand routine.

In humans, coronary angioplasty was performed in seven patients withgreater than 70% stenosis of the LAD coronary artery. The angioplastyinduced a three minute occlusion and reperfusion. Significant increasesin T-wave alternans occurred within two minutes of occlusion and withinten seconds of release/reperfusion. Alternans occurred predominantly inleads V₂, V₃ and V₄, corresponding to the sites overlying the ischemiczone. The alternans level was significantly greater than that observedin leads lI, V₁, V₅ and V₆ and in the Frank leads (see E. Frank, "Anaccurate, clinically practical system for spatial vectorcardiography,"Circulation, vol. 13, 1956, pp. 737-749). Alternation invariablyoccurred in the first half of the T-wave as predicted above.

FIG. 23 is a surface plot display obtained by the method of complexdemodulation (as set forth above) of the T-wave of the V₄ precordiallead during spontaneous heart rhythm in a representative patient duringangioplasty. As can be seen, within two minutes of occlusion there was asignificant increase in T-wave alternans which persisted throughout theocclusion. A marked surge in alternans upon reperfusion lasted less thanone minute.

FIG. 24 shows the level of T-wave alternans as a function of recordingsite in seven patients at three minutes of angioplasty-induced occlusionand upon balloon deflation. Alternans detected during occlusion in leadsV₂, V₃ and V₄ (the sites overlying the ischemic zone) was significantlygreater than in leads II, V₁, V₅ and V₆. During reperfusion, alternanslevels in leads V₁ -V₄ were significantly greater than in leads II, V₅and V₆.

The precordial leads may also be used to sense a plurality of ECGsignals for the measure of dispersion. Alternatively and as a compromiseto body mapping, a plurality of electrodes may be placed across thechest and back of a patient (e.g., 30 electrodes across the front and 30electrodes across the back) to optimize the measure of dispersion. Thiselectrode configuration of illustrated in FIGS. 25A and 25B. FIG. 25Aillustrates a possible electrode configuration for the chest. FIG. 25Billustrates a possible electrode configuration for the back.

CONCLUSION

The ability to sense alternans non-invasively from a surface ECG via theprecordial leads and to track the alternans dynamically yields a majoradvance in the quest for predicting SCD. Couple this with an analysis ofheart rate variability to determine the relative influence of thesympathetic and parasympathetic nervous systems and with a measure ofdispersion to improve the specificity of the alternans measure, and adiagnostic tool of unprecedented value in the field of cardiologyresults.

The inventors contemplate producing several indices for the analysis ofthe alternans, dispersion and heart rate variability data. These includea T-wave alternans index, a heart rate variability index, a dispersionindex and several cross-correlation indices. The T-wave alternans index(expressed in mV.msec) may be normalized for age, gender, medicalhistory, heart size, heart rate, etcetera. Tables of normal data for thealternans index could be established during exercise or behavioralstress tests. Monitored values of alternans could then be compared tothis standard index to yield diagnostic information on cardiac health.This includes detecting and locating ischemic or injured portions of theheart. Because of the regional nature of alternans, comparison of thealternans from each precordial lead with a corresponding standard indexvalue for that lead would allow an ischemic or injured site to belocated without the need for invasive procedures.

The alternans index may be developed along the lines of arterial bloodpressure indexes, for example, where pressure values in excess of 140mmHg/90 mmHg are deemed to be in the range where treatment is indicated.

The heart rate variability index may be expressed as an HF amplitude (inmilliseconds) and a LF/HF ratio. Normative data may be established forboth endpoints. It will be important to establish when sympatheticactivity is excessively high and/or when parasympathetic activity islow. In addition, the Very Low Frequency and Ultra Low Frequencyspectral portions of heart rate variability appear to be powerfulpredictors of arrhythmia which may be used to provide additionaldiagnostic information regarding myocardial infarction and SCD.

The cross-correlation index recognizes that a combination of high degreeof alternans and low heart rate variability indicates a condition iswhich the heart is particularly prone to ventricular fibrillation. Thisis based on the fact that lowered heart rate variability indicates highsympathetic and low parasympathetic activity. It is anticipated that amathematical function (e.g., a product of the alternans and heart ratevariability indices, a power function, etcetera) will be developed toproduce the cross-correlation index from the alternans index and theheart rate variability index. Empirical data will be required toestablish the precise quantitative relationship between the two. The useof ROC curves will establish a result with the highest sensitivity andspecificity in the prediction of sudden cardiac death.

It is contemplated that the invention will have great utility in thedevelopment of drugs, as their effects on autonomic activity and on theheart itself can be closely monitored.

It is further contemplated that the heart monitoring unit could beminiaturized and incorporated into an implantablecardioverter/defibrillator unit to sense alternans and heart ratevariability, and then deliver drugs or electricity to prevent or abortlife-threatening rhythms or to revert cardiac arrest.

Although the invention has been described and illustrated with a certaindegree of particularity, it is understood that those skilled in the artwill recognize a variety of applications and appropriate modificationswithin the spirit of the invention and the scope of the claims.

We claim:
 1. A method of assessing cardiac vulnerability comprising thesteps of:sensing a plurality of ECG signals from a plurality of sitesadjacent a heart; analyzing an amplitude of beat-to-beat alternation inT-waves of successive R-R intervals of at least one of said ECG signalsto obtain an alternans measure; analyzing a magnitude of heart ratevariability in successive R-R intervals of at least one of said ECGsignals to obtain a heart rate variability measure; analyzing amagnitude of dispersion of repolarization in a QT interval across atleast two of said plurality of ECG signals to obtain a dispersionmeasure; and simultaneously analyzing said alternans measure, said heartrate variability measure and said dispersion measure to assess cardiacvulnerability.
 2. The method of claim 1, wherein said step of sensing aplurality of ECG signals comprises, for each ECG signal:placing aprecordial ECG lead on the surface of a subject's body proximate to thesubject's heart to sense said ECG signal; amplifying said ECG signal;low-pass filtering said ECG signal; and sampling said ECG signal.
 3. Themethod of claim 1, wherein said step of analyzing an amplitude ofbeat-to-beat alternation comprises:selecting at least one of saidplurality of ECG signals; predicting the location in said ECG signal ofa T-wave in each R-R interval; partitioning each T-wave in said ECGsignal into a plurality of time divisions; summing the samples in eachof said time divisions of said selected ECG signal; forming a timeseries for each of said time divisions, each time series includingcorresponding sums from corresponding time divisions from successiveones of said T-waves; and performing dynamic estimation on each saidtime series to estimate the amplitude of beat-to-beat alternation foreach said time division.
 4. The method of claim 1, wherein said step ofanalyzing a magnitude of heart rate variability comprises:selecting atleast one of said plurality of ECG signals; locating the peak amplitudein each R-R interval to find the apex of each R-wave in said selectedECG signal; calculating the time between successive R-waves to determinea magnitude of each said R-R interval; forming a time series with saidmagnitudes of said R-R intervals; performing dynamic estimation on saidtime series to estimate a magnitude of a high frequency component ofheart rate variability and to estimate a magnitude of a low frequencycomponent of heart rate variability; and forming a ratio of saidmagnitudes of said low frequency and said high frequency components ofheart rate variability, said ratio indicating sympathetic activity. 5.The method of claim 1, wherein said step of analyzing dispersion ofrepolarization comprises:for each of said plurality of ECGsignals,locating the peak amplitude in each R-R interval to find theapex of each R-wave, determining the temporal location of the beginningof each Q-wave based on the apex of each R-wave, determining thetemporal location of the end of each T-wave, and calculating each QTinterval as a time difference from the beginning of the Q-wave to theend of the T-wave; and estimating a measure of dispersion ofrepolarization of said QT intervals across said plurality of ECGsignals.
 6. The method of claim 5, wherein said step of estimating ameasure of dispersion comprises:calculating a maximum difference betweensaid QT intervals taken across said plurality of ECG signals to estimatesaid measure of dispersion.
 7. The method of claim 5, wherein said stepof estimating a measure of dispersion comprises:calculating each R-Rinterval as a time difference between successive R-waves; using each R-Rinterval to correct a corresponding QT interval to produce a correctedQT interval for each QT interval; and calculating a maximum differencebetween said corrected QT intervals taken across said plurality of ECGsignals to estimate said measure of dispersion.
 8. The method of claim5, wherein said step of estimating a measure of dispersioncomprises:averaging said QT intervals to produce an average QT interval;dividing each QT interval by said average QT interval to produce a QTratio for each QT interval; averaging said QT ratios to produce anaverage QT ratio; and calculating a standard deviation of said QT ratioto estimate said measure of dispersion.
 9. The method of claim 5,wherein said step of estimating a measure of dispersioncomprises:calculating each R-R interval as a time difference betweensuccessive R-waves; using each R-R interval to correct a correspondingQT interval to produce a corrected QT interval for each QT interval;averaging said corrected QT intervals to produce an average corrected QTinterval; dividing each corrected QT interval by said average correctedQT interval to produce corrected QT ratios; averaging said corrected QTratios to produce an average corrected QT ratio; and calculating astandard deviation of said corrected QT ratio to estimate said measureof dispersion.
 10. The method of claim 5, wherein said step ofestimating a measure of dispersion comprises:calculating, for each R-Rinterval across said plurality of ECG signals, an average ECG signal;calculating, for each R-R interval of said plurality of ECG signals, anRMS deviation using said average ECG signal; and taking an amplitude ofa maximum one of said RMS deviations as said measure of dispersion. 11.The method of claim 1, further comprising the step of:analyzinginstantaneous heart rate, arterial blood pressure and instantaneous lungvolume to obtain a measure of baroreceptor sensitivity, and wherein saidstep of simultaneously analyzing further includes analyzing said measureof baroreceptor sensitivity to assess cardiac vulnerability.
 12. Themethod of claim 11, wherein said step of analyzing instantaneous heartrate, arterial blood pressure and instantaneous lung volumecomprises:(1) selecting at least one of said plurality of ECG signals;(2) sensing and digitizing a blood pressure signal representing arterialblood pressure; (3) sensing and digitizing a respiration signalrepresenting instantaneous lung volume; (4) computing an instantaneousheart rate for each R-R interval in said selected ECG signal; and (5)using said heart rate, said blood pressure signal and said respirationsignal to determine said measure of baroreceptor sensitivity.
 13. Amethod of predicting susceptibility to sudden cardiac death, comprisingthe steps of:(a) analyzing at least one of a beat-to-beat alternation ina T-wave of an ECG of a patient's heart and dispersion of repolarizationin said ECG of the patient's heart to assess cardiac electricalstability; and (b) analyzing at least one of a magnitude of heart ratevariability in said ECG of the patient's heart and baroreceptorsensitivity to assess autonomic influence on the patient's heart,provided that alternation and heart rate variability are not analyzed incombination without at least one of dispersion and baroreceptorsensitivity also being analyzed; and (c) performing steps (a) and (b)simultaneously to assess the patient's risk of sudden cardiac death. 14.The method of claim 13, wherein said step (a) of analyzingcomprises:analyzing both beat-to-beat alternation and dispersion ofrepolarization to assess cardiac electrical stability.
 15. The method ofclaim 14 wherein said step (b) of analyzing comprises:analyzing bothsaid magnitude of heart rate variability and said baroreceptorsensitivity to assess autonomic influence on the heart.
 16. The methodof claim 14 wherein said step of analyzing beat-to-beat alternation,comprises:(1) sensing an ECG signal from the patient's heart, said ECGsignal having a plurality of R-R intervals; (2) digitizing said ECGsignal; (3) predicting the location in said ECG signal of said T-wave ineach R-R interval; (4) partitioning each T-wave in said ECG signal intoa plurality of time divisions; (5) summing the samples in each of saidtime divisions of said ECG signal; (6) forming a time series for each ofsaid time divisions, each time series including corresponding sums fromcorresponding time divisions from successive ones of said T-waves; and(7) performing dynamic estimation on each said time series to estimatethe amplitude of beat-to-beat alternation for each said time division.17. The method of claim 16 wherein said step of analyzing dispersion ofrepolarization comprises:(1) sensing a plurality of ECG signals from aplurality of sites adjacent a heart, each of said plurality of ECGsignals having a plurality of R-R intervals; (2) for each of saidplurality of ECG signals,i) locating the peak amplitude in each R-Rinterval to find the apex of each R-wave, ii) determining the temporallocation of the beginning of each Q-wave based on the apex of eachR-wave, iii) determining the temporal location of the end of eachT-wave, and iv) calculating each QT interval as a time difference fromthe beginning of the Q-wave to the end of the T-wave; and (3) estimatinga measure of dispersion of repolarization of said QT intervals acrosssaid plurality of ECG signals.
 18. The method of claim 17 wherein saidstep (b) of analyzing comprises:analyzing both said magnitude of heartrate variability and said baroreceptor sensitivity to assess autonomicinfluence on the heart.
 19. The method of claim 18, wherein said step ofanalyzing heart rate variability comprises:(1) sensing an ECG signalfrom the patient's heart, said ECG signal having a plurality of R-Rintervals; (2) digitizing said ECG signal; (3) locating the peakamplitude in each R-R interval to find the apex of each R-wave in saidECG signal; (4) calculating the time between successive R-waves todetermine a magnitude of each said R-R interval; (5) forming a timeseries with said magnitudes of said R-R intervals; (6) performingdynamic estimation on said time series to estimate a magnitude of a highfrequency component of heart rate variability and to estimate amagnitude of a low frequency component of heart rate variability; and(7) forming a ratio of said magnitudes of said low frequency and saidhigh frequency components of heart rate variability, said ratioindicating sympathetic activity.
 20. The method of claim 19, whereinsaid step of analyzing baroreceptor sensitivity comprises:(1) sensingand digitizing an ECG signal from the patient's heart, said ECG signalhaving a plurality of R-R intervals; (2) sensing and digitizing a bloodpressure signal representing arterial blood pressure; (3) sensing anddigitizing a respiration signal representing instantaneous lung volume;(4) computing an instantaneous heart rate for each R-R interval in saidECG signal; and (5) using said heart rate, said blood pressure signaland said respiration signal to determine said baroreceptor sensitivity.21. The method of claim 13, wherein said step (b) of analyzingcomprises:analyzing both said magnitude of heart rate variability andsaid baroreceptor sensitivity to assess autonomic influence on theheart.
 22. An apparatus for predicting susceptibility to sudden cardiacdeath, comprising:first means for analyzing at least one of abeat-to-beat alternation in a T-wave of an ECG of a patient's heart anddispersion of repolarization in said ECG of the patient's heart toassess cardiac electrical stability; and second means for analyzing atleast one of a magnitude of heart rate variability in said ECG of thepatient's heart and baroreceptor sensitivity to assess autonomicinfluence on the patient's heart, provided that alternation and heartrate variability are not analyzed in combination with at least one ofdispersion and baroreceptor sensitivity also be analyzed; and thirdmeans for simultaneously analyzing assessment of cardiac electricalstability from said first means and autonomic influence on the patient'sheart from said second means to predict the patient's risk of suddencardiac death.
 23. The apparatus of claim 22, wherein said first meanscomprises:fourth means for analyzing beat-to-beat alternation to assesscardiac electrical stability; and fifth means for analyzing dispersionof repolarization to assess cardiac electrical stability.
 24. Theapparatus of claim 23, wherein said second means comprises:sixth meansfor analyzing said magnitude of heart rate variability to assessautonomic influence on the heart; and seventh means for analyzing saidmagnitude of heart rate variability to assess autonomic influence on theheart.
 25. The apparatus of claim 24, further comprising:means forsensing a plurality of ECG signals from a plurality of sites adjacent aheart, each of said plurality of ECG signals having a plurality of R-Rintervals; and means for digitizing said plurality of ECG signals. 26.The apparatus of claim 25, wherein said fourth means comprises:means forpredicting the location in a selected ECG signal of a T-wave in each R-Rinterval; means for partitioning each T-wave in said selected ECG signalinto a plurality of time divisions; means for summing the samples ineach of said time divisions of said selected ECG signal; means forforming a time series for each of said time divisions, each time seriesincluding corresponding sums from corresponding time divisions fromsuccessive ones of said T-waves; and means for dynamically estimating oneach said time series to estimate the amplitude of beat-to-beatalternation for each said time division.
 27. The apparatus of claim 26,wherein said fifth means comprises:means for locating the peak amplitudein each R-R interval to find the apex of each R-wave in each of saidplurality of ECG signals; means for determining the temporal location ofthe beginning of each Q-wave based on the apex of each R-wave; means fordetermining the temporal location of the end of each T-wave; means forcalculating each QT interval as a time difference from the beginning ofthe Q-wave to the end of the T-wave; and means for estimating a measureof dispersion of repolarization of said QT intervals across saidplurality of ECG signals.
 28. The apparatus of claim 27, wherein saidsixth means comprises:means for locating the peak amplitude in each R-Rinterval of a selected ECG signal to find the apex of each R-wave; meansfor calculating the time between successive R-waves to determine amagnitude of each said R-R interval; means for forming a time serieswith said magnitudes of said R-R intervals; means for performing dynamicestimation on said time series to estimate a magnitude of a highfrequency component of heart rate variability and to estimate amagnitude of a low frequency component of heart rate variability; andmeans for forming a ratio of said magnitudes of said low frequency andsaid high frequency components of heart rate variability, said ratioindicating sympathetic activity.
 29. The apparatus of claim 28, whereinsaid seventh means comprises:sensing and digitizing a blood pressuresignal representing arterial blood pressure; sensing and digitizing arespiration signal representing instantaneous lung volume; computing aninstantaneous heart rate for each R-R interval in a selected ECG signal;and using said heart rate, said blood pressure signal and saidrespiration signal to determine said baroreceptor sensitivity.